用支持向量机构建多分类斜决策树，提升分类精度与模型紧凑性。
ODTE -- An ensemble of multi-class SVM-based oblique decision trees
- 以SVM定义决策节点中的超平面，实现多分类直接划分。
- 在49个数据集上优于现有方法，调参后性能显著提升。
- 生成的决策树更紧凑，适合资源受限场景使用。
我们提出ODTE，一种基于斜决策树的新型集成学习模型。其基础算法STree利用支持向量机在决策节点中定义超平面，通过一对多或一对一策略直接处理多分类任务，无需将样本聚类为二元组。每个决策节点仅保留使类别不纯度最小化的最佳SVM模型，即使该SVM仅解决子二分类问题。在49个数据集上，对比多种先进斜决策树集成算法的实验表明，ODTE性能持续领先，调参后获得显著提升。此外，由STree生成的斜决策树比其他算法更紧凑。
原文摘要 · Abstract (English)
We propose ODTE, a new ensemble that uses oblique decision trees as base classifiers. Additionally, we introduce STree, the base algorithm for growing oblique decision trees, which leverages support vector machines to define hyperplanes within the decision nodes. We embed a multiclass strategy -- one-vs-one or one-vs-rest -- at the decision nodes, allowing the model to directly handle non-binary classification tasks without the need to cluster instances into two groups, as is common in other approaches from the literature. In each decision node, only the best-performing model SVM -- the one that minimizes an impurity measure for the n-ary classification -- is retained, even if the learned SVM addresses a binary classification subtask. An extensive experimental study involving 49 datasets and various state-of-the-art algorithms for oblique decision tree ensembles has been conducted. Our results show that ODTE ranks consistently above its competitors, achieving significant performance gains when hyperparameters are carefully tuned. Moreover, the oblique decision trees learned through STree are more compact than those produced by other algorithms evaluated in our experiments.
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